Cortical sulci model and matching from 3D brain magnetic resonance images

Positron emission tomography (PET) is one of the most popular techniques for the study of brain functional activity. Several studies show that PET is an in-vivo examination technique able to produce real images of cerebral activity, and is also neither destructive nor invasive. Unfortunately, PET images offer low resolution and signal-to-noise ratio. Moreover, they do not reflect the anatomy of patients. Accurate and reproducible analysis of PET images requires other informations, coming from aliases or other images such as magnetic resonance images (MRI) of the same patient. Hence it is of great interest to superimpose functional PET data and anatomical MRI data. Here, the authors deal with representation and identification of sulci. A first step is to choose and to automatically extract anatomical knowledge from a database, in order to adapt it to any image where the recognition has to be performed. Then, the authors introduce a stochastic method using these features to recognise human cerebral sulci.